The objectives of this research are to propose an approate and efficient model for analyzing rice corn and sugar yields data. A linear mixed model (LMM) with spatial correlation following the conditional autoregressive model (CAR) was adopted. The results show that for rice yields if the amount of rainfall in a harvest season increases 1 mm the yields will decease by 25.74 tons. if the temperature in the harvest season increases 1 Celsius the yields will decease by 212.60 tons and according to the trend the yield will increase by 15.80 tons each year. For sugar cane yields if the amount of rainfall in the harvest season increases 1 mm the yields will decease by 51.35 tons. if the temperature in the harvest season increase 1 Celsius the yields will incease by 121.30 tons and according to the trend the yield will increase by 29.93.80 tons each year. For corn yield if the amount of rainfall in the havest season increases 1 mm the yields will incease by 73.45 tons. if the temperature in he havest season increases 1 celsius the yields will decease by 40.44 tons and according to the trend the yield will increase by 1.74 tons each year.

The objectives of this research are to estimate the morbidity rates of Leptospirosis in each month of all provinces in Thailand to determine the trend of leptospirosis over time to investigate factors influencing on the morbidity rates and to construct the disease maps of the leptospirosis. The generalized linear mixed model (GLMM) in which the spatial effects follow the conditional autoregressive model (CAR) and the temporal effects follow a linear trend. The estimated morbidity rates are used to construct the disease maps. The dependent variables are the numbers of leptospirosis patients in each month of each province and are assumed to have a Poisson distribution. The data are secondary data at a provincial level. The factors considered are rainfall averaged temperatures and regions. The results show that the factors influencing on the morbidity rates are rainfall (Relative Risk (RR) = 1.0013) averaged temperature (RR = 0.9922) north region (RR = 10.6440) northeast region (RR = 14.3249) southern region (RR = 15.5491) western region (RR = 3.6219) and eastern region (RR = 1.0103) where the central region is a reference region and a linear trend (RR = 1.0183). The top ten province and months with high morbidity rates (per 1000000 population) ranking from largest to smallest values are Phangnga in September (59.00) Phangnga in August (33.49) Sisaket in October (23.14) Sisaket in August ศรีสะเกษ (22.60) Sisaket in September (22.57) Phngnga in October (22.55) Loei in September (21.11) Phangnga in July (20.81) Phangnga in May (20.72) and Phangnga in June (20.47) respectively. The leptospirosis maps are easy for readers to identify which areas are at high risk. They are a useful tool for planning and controlling the leptospirosis.

The objective of this research is to select a proper cumulative function for trend in an analysis of simultaneous multiple time series data with autocorrelation trend and outliers using a Bayesian method. The proposed model is applied to fluctuate stock price data in the stock exchange of Thailand. The data consist of the closing prices of 5 stocks. The algorithms are written in OpenBUGS and the performance of the model is evaluated via a simulation study in R. The cumulative distribution functions considered here are a Weibull cumulative distribution function and an exponential cumulative distribution. They are compared using RMSE MAPE and MAE as criteria. The research found that an exponential cumulative function for trends gives smaller values of the three criteria in both model fitting and model validation for the4 stocks EA NWR and KTB. However a Weibull cumulative distribution function gives smaller values of three criteria for the PTT in both model fitting and model validation.

The objectives of this research are to estimate the morbidity rates of Hypertension and Ischemic heart disease in each province of Thailand to investigate factors influencing on the morbidity rates and to construct the disease maps of those two diseases. The generalized linear mixed model (GLMM) in which the responses have a Poisson distribution and the spatial effects are a multivariate conditional autoregressive model (MCAR) are adopted. The provincial secondary data sets consisting of a number of Hypertension and Ischemic heart disease patients an average income per head and a student proportion were collected. The results show the average income per head (Relative Risk (RR) = 1.0002) and student proportion (RR = 1.0186) influence the morbidity rates of Hypertension and Ischemic heart disease. Hypertension and Ischemic maps are easy for readers to see which areas are at high risk. They are a useful tool for planning and controlling the Hypertension and Ischemic heart disease.

The objectives of this research are to estimate the morbidity rate of Dengue Hemorrhagic fever in each province of Thailand and to construct the map of the Dengue Hemorrhagic fever. A Bayesian model is used for the data analysis. The data are mathematical spatial correlated in the form of the conditional autoregressive model (CAR). The secondary data were collected at a provincial level. They were the number of Dengue Hemorrhagic fever patients rainfall and average temperature. The research found that the rainfall (RR = 1.0026) and the average temperature (RR = 1.2681) had influence on the morbidity of Dengue Hemorrhagic fever. The season during July to September had more influence on the morbidity of Dengue Hemorrhagic fever than during the other seasons. The top 5 highest morbidity rates per 100000 population were Chiangmai in October (1288.00) July (1096.00) September (804.30) Surin in October (772.30) and Nakhon Si Thammarat in August (666.20). The disease map constructed for showing the distribution of the disease is easy to understand and useful for public health.